Recommendation

Recommendation inference generates a ranked list of items for users (or similar users/items for a query). Provide user-item interaction data in the same format used during training.

Available Models

• Matrix Factorization (SVD) – Collaborative filtering via singular value decomposition
• Matrix Factorization (sklearn) – Sklearn-based sparse SVD matrix factorization
• Item-Based KNN – Recommend based on item-to-item similarity
• User-Based KNN – Recommend based on user-to-user similarity
• Content-Based TF-IDF – Recommend based on item text/metadata similarity
• Embeddings Similarity – Dense embedding-based item similarity
• BERT4Rec – Sequential recommendation using BERT-style self-attention
• Hybrid CF + CB – Combines collaborative filtering with content-based signals
• Association Rules – Recommend based on frequent item co-occurrence patterns